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onboarding

Analyze an unfamiliar codebase and generate a structured onboarding guide with architecture map, key entry points, conventions, and a starter CLAUDE.md.

Skill by jeanchastel

IndexedMIT
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

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About

Analyze an unfamiliar codebase and generate a structured onboarding guide with architecture map, key entry points, conventions, and a starter CLAUDE.md. Use when joining a new project or setting up Claude Code for the first time in a repo.

Use cases: agent-building

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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

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  • codeload.github.com · found in install command

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  • ~/.claude/skills/onboarding

What's inside

Skill name
onboarding
Description
Analyze an unfamiliar codebase and generate a structured onboarding guide with architecture map, key entry points, conventions, and a starter CLAUDE.md. Use when joining a new project or setting up Claude Code for the first time in a repo.
License
MIT
Instructions
139 lines in SKILL.md · view on GitHub

Files (2)

  • SKILL.md
  • templates.md · references/

SKILL.md (first lines)

# Codebase Onboarding

Systematically analyze an unfamiliar codebase and produce a structured onboarding guide. Designed for developers joining a new project or setting up Claude Code in an existing repo for the first time.

## When to Use

- First time opening a project with Claude Code
- Joining a new team or repository
- User asks "help me understand this codebase"
- User asks to generate a CLAUDE.md for a project
- User says "onboard me" or "walk me through this repo"

## How It Works

### Phase 1: Reconnaissance

Gather raw signals about the project without reading every file. Run these checks in parallel:

```
1. Package manifest detection
   → package.json, go.mod, Cargo.toml, pyproject.toml, pom.xml, build.gradle,
     Gemfile, composer.json, mix.exs, pubspec.yaml

2. Framework fingerprinting
   → next.config.*, nuxt.config.*, angular.json, vite.config.*,
     django settings, flask app factory, fastapi main, rails config

3. Entry point identification
   → main.*, index.*, app.*, server.*, cmd/, src/main/

4. Directory structure snapshot
   → Top 2 levels of the directory tree, ignoring node_modules, vendor,
     .git, dist, build, __pycache__, .next

5. Config and tooling detection
   → .eslintrc*, .prettierrc*, tsconfig.json, Makefile, Dockerfile,
     docker-compose*, .github/workflows/, .env.example, CI configs

6. Test structure detection
   → tests/, test/, __tests__/, *_test.go, *.spec.ts, *.test.js,
     pytest.ini, jest.config.*, vitest.config.*
```

### Phase 2: Architecture Mapping

From the reconnaissance data, identify:

**Tech Stack**
- Language(s) and version constraints
- Framework(s) and major libraries
- Database(s) and ORMs
- Build tools and bundlers
- CI/CD platform

**Architecture Pattern**
- Monolith, monorepo, microservices, or serverless
- Frontend/backend split or full-stack
- API style: REST, GraphQL, gRPC, tRPC

**Key Directories**
Map the top-level directories to their purpose:

<!-- Example for a React project: replace with detected directories -->
```
src/components/  → React UI components
src/api/         → API route handlers
src/lib/         → Shared utilities
src/db/          → Database models and migrations
tests/           → Test suites
scripts/         → Build and deployment scripts
```

**Data Flow**
Trace one request from entry to response:
- Where does a request enter? (router, handler, controller)
- How is it validated? (middleware, schemas, guards)
- Where is business logic? (services, models, use cases)
- How does it reach the database? (ORM, raw queries, repositories)

### Phase 3: Convention Detection

Identify patterns the codebase already follows:

**Naming Conventions**
- File naming: kebab-case, camelCase, PascalCase, snake_case
- Component/class naming patterns
- Test file naming: `*.test.ts`, `*.spec.ts`, `*_test.go`

**Code Patterns**
- Error handling style: try/catch, Result types, error codes
- Dependency injection or direct imports
- State management approach
- Async patterns: callbacks, promises, async/await, channels

**Git Conventions**
- Branch naming from recent branches
- Commit message style from recent commits
- PR workflow (squash, merge, rebase)
- If the repo has no commits yet or only a shallow history (e.g. `git clone --depth 1`), skip this section and note "Git history unavailable or too shallow to detect conventions"

### Phase 4: Generate Onboarding Artifacts

Produce two outputs:

The full onboarding-guide and CLAUDE.md output templates live in
`references/templates.md`.

In an Arkira repository, `CLAUDE.md` is a sync-installed pointer-only
overlay to `AGENTS.md`. A `.arkira/config.json` file present on disk
(whether or not yet committed, since `/arkira-init` writes it before the
first onboarding-generated commit) signals this. Write the Starter
CLAUDE.md content into the user-owned area of `AGENTS.md` instead, and
leave `CLAUDE.md` as the pointer; the next `/arkira-sync` would otherwise
overwrite a content-bearing `CLAUDE.md`. In any other repository, write the
Starter CLAUDE.md content to `CLAUDE.md` directly.

The active host writes the target file directly by default. Optional
delegation uses
`bin/arkira run ai-engineering/runtime/role-run.sh <repo> executor code_editing --prompt-file <file>`.

From jeanchastel/arkira, MIT.

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Last commit (this folder)
Oct 23, 2026 (6 days ago)
Last release
Oct 23, 2026 (5 days ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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Cite this page

onboarding skill. Agent Mart. https://agentmart.dev/skills/jeanchastel-onboarding (last updated 2026-10-24)